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Last data update: 2014.03.03 |
Cross TabulationDescriptionOutput well-formatted cross tabulation. Also can genarate latex syntax of cross tabulation. UsagecrossTable(..., deparse.level = 2) ## S3 method for class 'CrossTable' summary(object, digits=3, latex=FALSE, ...) Arguments
Value
Author(s)Masahiro Hayashi Examples
sex <- factor(rbinom(1:1000, 1, 0.5), labels=c("male" , "female"))
age <- factor(rbinom(1:1000, 2, 0.4), labels=c("young", "middle", "old"))
weight <- factor(rbinom(1:1000, 2, 0.6), labels=c("light", "middle", "heavy"))
cross.table1 <- crossTable(sex, age)
summary(cross.table1)
cross.table2 <- crossTable(sex, age, weight)
summary(cross.table2)
summary(cross.table2, latex = TRUE)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Rz)
Loading required package: grid
Loading required package: foreign
Loading required package: memisc
Loading required package: lattice
Loading required package: MASS
Attaching package: 'memisc'
The following objects are masked from 'package:stats':
contr.sum, contr.treatment, contrasts
The following object is masked from 'package:base':
as.array
Loading required package: psych
Loading required package: ggplot2
Attaching package: 'ggplot2'
The following objects are masked from 'package:psych':
%+%, alpha
################################ Rz ################################
Excute Rz() to start,
or you can start from menu bar if you use R on Windows.
####################################################################
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Rz/crossTable.Rd_%03d_medium.png", width=480, height=480)
> ### Name: crossTable
> ### Title: Cross Tabulation
> ### Aliases: crossTable summary.CrossTable
> ### Keywords: cross tabulation
>
> ### ** Examples
>
> sex <- factor(rbinom(1:1000, 1, 0.5), labels=c("male" , "female"))
> age <- factor(rbinom(1:1000, 2, 0.4), labels=c("young", "middle", "old"))
> weight <- factor(rbinom(1:1000, 2, 0.6), labels=c("light", "middle", "heavy"))
>
> cross.table1 <- crossTable(sex, age)
> summary(cross.table1)
==================================
age
--------------------
sex young middle old Total
----------------------------------
male 187 247 76 510
36.7% 48.4% 14.9% 100%
female 173 248 69 490
35.3% 50.6% 14.1% 100%
----------------------------------
Total 360 495 145 1000
36.0% 49.5% 14.5% 100%
==================================
Chi-Square Test for Independence
Number of cases in table: 1000
Number of factors: 2
Test for independence of all factors:
Chisq = 0.4846, df = 2, p-value = 0.7848
X^2 df P(> X^2)
Likelihood Ratio 0.48464 2 0.78481
Pearson 0.48459 2 0.78482
Phi-Coefficient : NA
Contingency Coeff.: 0.022
Cramer's V : 0.022
>
> cross.table2 <- crossTable(sex, age, weight)
> summary(cross.table2)
=========================================
weight
--------------------
sex age light middle heavy Total
-----------------------------------------
male young 27 97 63 187
14.44% 51.9% 33.7% 100%
middle 39 115 93 247
15.79% 46.6% 37.7% 100%
old 5 44 27 76
6.58% 57.9% 35.5% 100%
----------------------------------
Total 71 256 183 510
13.92% 50.2% 35.9% 100%
-----------------------------------------
female young 30 84 59 173
17.3% 48.6% 34.1% 100%
middle 46 126 76 248
18.5% 50.8% 30.6% 100%
old 9 32 28 69
13.0% 46.4% 40.6% 100%
----------------------------------
Total 85 242 163 490
17.3% 49.4% 33.3% 100%
-----------------------------------------
Total young 57 181 122 360
15.83% 50.3% 33.9% 100%
middle 85 241 169 495
17.17% 48.7% 34.1% 100%
old 14 76 55 145
9.66% 52.4% 37.9% 100%
----------------------------------
Total 156 498 346 1000
15.60% 49.8% 34.6% 100%
=========================================
Chi-Square Test for Independence
sex : male
Number of cases in table: 510
Number of factors: 2
Test for independence of all factors:
Chisq = 5.72, df = 4, p-value = 0.2211
X^2 df P(> X^2)
Likelihood Ratio 6.3897 4 0.17187
Pearson 5.7199 4 0.22106
Phi-Coefficient : NA
Contingency Coeff.: 0.105
Cramer's V : 0.075
sex : female
Number of cases in table: 490
Number of factors: 2
Test for independence of all factors:
Chisq = 2.8532, df = 4, p-value = 0.5827
X^2 df P(> X^2)
Likelihood Ratio 2.8605 4 0.58144
Pearson 2.8532 4 0.58269
Phi-Coefficient : NA
Contingency Coeff.: 0.076
Cramer's V : 0.054
Total
Number of cases in table: 1000
Number of factors: 2
Test for independence of all factors:
Chisq = 4.968, df = 4, p-value = 0.2906
X^2 df P(> X^2)
Likelihood Ratio 5.4475 4 0.24438
Pearson 4.9676 4 0.29064
Phi-Coefficient : NA
Contingency Coeff.: 0.07
Cramer's V : 0.05
>
> summary(cross.table2, latex = TRUE)
egin{table}[htbp]
centering
caption{sex $\times$ age $\times$ weight}
egin{tabular}{llrrrr}
\toprule
& & multicolumn{3}{c}{weight} & \
cline{3-5}
sex &age &multicolumn{1}{c}{light}&multicolumn{1}{c}{middle}&multicolumn{1}{c}{heavy}&multicolumn{1}{c}{Total} \
midrule
male &young & 27 & 97 & 63 & 187 \
& & 14.44% & 51.9% & 33.7% & 100% \
&middle& 39 & 115 & 93 & 247 \
& & 15.79% & 46.6% & 37.7% & 100% \
&old & 5 & 44 & 27 & 76 \
& & 6.58% & 57.9% & 35.5% & 100% \
midrule
&Total & 71 & 256 & 183 & 510 \
& & 13.92% & 50.2% & 35.9% & 100% \
midrule
female&young & 30 & 84 & 59 & 173 \
& & 17.3% & 48.6% & 34.1% & 100% \
&middle& 46 & 126 & 76 & 248 \
& & 18.5% & 50.8% & 30.6% & 100% \
&old & 9 & 32 & 28 & 69 \
& & 13.0% & 46.4% & 40.6% & 100% \
midrule
&Total & 85 & 242 & 163 & 490 \
& & 17.3% & 49.4% & 33.3% & 100% \
midrule
Total &young & 57 & 181 & 122 & 360 \
& & 15.83% & 50.3% & 33.9% & 100% \
&middle& 85 & 241 & 169 & 495 \
& & 17.17% & 48.7% & 34.1% & 100% \
&old & 14 & 76 & 55 & 145 \
& & 9.66% & 52.4% & 37.9% & 100% \
midrule
&Total & 156 & 498 & 346 & 1000 \
& & 15.60% & 49.8% & 34.6% & 100% \
ottomrule
end{tabular}
end{table}
Chi-Square Test for Independence
sex : male
Number of cases in table: 510
Number of factors: 2
Test for independence of all factors:
Chisq = 5.72, df = 4, p-value = 0.2211
X^2 df P(> X^2)
Likelihood Ratio 6.3897 4 0.17187
Pearson 5.7199 4 0.22106
Phi-Coefficient : NA
Contingency Coeff.: 0.105
Cramer's V : 0.075
sex : female
Number of cases in table: 490
Number of factors: 2
Test for independence of all factors:
Chisq = 2.8532, df = 4, p-value = 0.5827
X^2 df P(> X^2)
Likelihood Ratio 2.8605 4 0.58144
Pearson 2.8532 4 0.58269
Phi-Coefficient : NA
Contingency Coeff.: 0.076
Cramer's V : 0.054
Total
Number of cases in table: 1000
Number of factors: 2
Test for independence of all factors:
Chisq = 4.968, df = 4, p-value = 0.2906
X^2 df P(> X^2)
Likelihood Ratio 5.4475 4 0.24438
Pearson 4.9676 4 0.29064
Phi-Coefficient : NA
Contingency Coeff.: 0.07
Cramer's V : 0.05
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